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Title: Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem
Authors: Consoli, S
Darby-Dowman, K
Mladenović, N
Moreno-Pérez, JA
Keywords: Metaheuristics;Combinatorial optimization;Minimum labelling spanning tree;Variable neighbourhood search;Greedy randomized adaptive search procedure
Issue Date: 2007
Publisher: Brunel University
Citation: Department of Mathematical Sciences Technical Reports, TR/01/07, May 2007
Abstract: This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics.
Appears in Collections:Dept of Mathematics Research Papers

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